Assignment 03

Author

Affiliation

Makenzie Howard

Boston University

Published

September 22, 2025

Modified

September 23, 2025

1 Load the Dataset

import pandas as pd
import plotly.express as px
import plotly.io as pio
from pyspark.sql import SparkSession
import re
import numpy as np
import plotly.graph_objects as go
from pyspark.sql.functions import col, split, explode, regexp_replace, transform, when
from pyspark.sql import functions as F
from pyspark.sql.functions import col, monotonically_increasing_id

np.random.seed(42)

pio.renderers.default = "notebook"

# Initialize Spark Session
spark = SparkSession.builder.appName("LightcastData").getOrCreate()

# Load Data
df = spark.read.option("header", "true").option("inferSchema", "true").option("multiLine","true").option("escape", "\"").csv("lightcast_job_postings.csv")
df.createOrReplaceTempView("job_postings")

# Show Schema and Sample Data
print("---This is Diagnostic check, No need to print it in the final doc---")

df.printSchema() # comment this line when rendering the submission
df.show(5)

WARNING: Using incubator modules: jdk.incubator.vector
Using Spark's default log4j profile: org/apache/spark/log4j2-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
25/09/23 21:54:24 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
[Stage 0:>                                                          (0 + 1) / 1]                                                                                [Stage 1:>                                                          (0 + 1) / 1]                                                                                25/09/23 21:54:40 WARN SparkStringUtils: Truncated the string representation of a plan since it was too large. This behavior can be adjusted by setting 'spark.sql.debug.maxToStringFields'.

---This is Diagnostic check, No need to print it in the final doc---
root
 |-- ID: string (nullable = true)
 |-- LAST_UPDATED_DATE: string (nullable = true)
 |-- LAST_UPDATED_TIMESTAMP: timestamp (nullable = true)
 |-- DUPLICATES: integer (nullable = true)
 |-- POSTED: string (nullable = true)
 |-- EXPIRED: string (nullable = true)
 |-- DURATION: integer (nullable = true)
 |-- SOURCE_TYPES: string (nullable = true)
 |-- SOURCES: string (nullable = true)
 |-- URL: string (nullable = true)
 |-- ACTIVE_URLS: string (nullable = true)
 |-- ACTIVE_SOURCES_INFO: string (nullable = true)
 |-- TITLE_RAW: string (nullable = true)
 |-- BODY: string (nullable = true)
 |-- MODELED_EXPIRED: string (nullable = true)
 |-- MODELED_DURATION: integer (nullable = true)
 |-- COMPANY: integer (nullable = true)
 |-- COMPANY_NAME: string (nullable = true)
 |-- COMPANY_RAW: string (nullable = true)
 |-- COMPANY_IS_STAFFING: boolean (nullable = true)
 |-- EDUCATION_LEVELS: string (nullable = true)
 |-- EDUCATION_LEVELS_NAME: string (nullable = true)
 |-- MIN_EDULEVELS: integer (nullable = true)
 |-- MIN_EDULEVELS_NAME: string (nullable = true)
 |-- MAX_EDULEVELS: integer (nullable = true)
 |-- MAX_EDULEVELS_NAME: string (nullable = true)
 |-- EMPLOYMENT_TYPE: integer (nullable = true)
 |-- EMPLOYMENT_TYPE_NAME: string (nullable = true)
 |-- MIN_YEARS_EXPERIENCE: integer (nullable = true)
 |-- MAX_YEARS_EXPERIENCE: integer (nullable = true)
 |-- IS_INTERNSHIP: boolean (nullable = true)
 |-- SALARY: integer (nullable = true)
 |-- REMOTE_TYPE: integer (nullable = true)
 |-- REMOTE_TYPE_NAME: string (nullable = true)
 |-- ORIGINAL_PAY_PERIOD: string (nullable = true)
 |-- SALARY_TO: integer (nullable = true)
 |-- SALARY_FROM: integer (nullable = true)
 |-- LOCATION: string (nullable = true)
 |-- CITY: string (nullable = true)
 |-- CITY_NAME: string (nullable = true)
 |-- COUNTY: integer (nullable = true)
 |-- COUNTY_NAME: string (nullable = true)
 |-- MSA: integer (nullable = true)
 |-- MSA_NAME: string (nullable = true)
 |-- STATE: integer (nullable = true)
 |-- STATE_NAME: string (nullable = true)
 |-- COUNTY_OUTGOING: integer (nullable = true)
 |-- COUNTY_NAME_OUTGOING: string (nullable = true)
 |-- COUNTY_INCOMING: integer (nullable = true)
 |-- COUNTY_NAME_INCOMING: string (nullable = true)
 |-- MSA_OUTGOING: integer (nullable = true)
 |-- MSA_NAME_OUTGOING: string (nullable = true)
 |-- MSA_INCOMING: integer (nullable = true)
 |-- MSA_NAME_INCOMING: string (nullable = true)
 |-- NAICS2: integer (nullable = true)
 |-- NAICS2_NAME: string (nullable = true)
 |-- NAICS3: integer (nullable = true)
 |-- NAICS3_NAME: string (nullable = true)
 |-- NAICS4: integer (nullable = true)
 |-- NAICS4_NAME: string (nullable = true)
 |-- NAICS5: integer (nullable = true)
 |-- NAICS5_NAME: string (nullable = true)
 |-- NAICS6: integer (nullable = true)
 |-- NAICS6_NAME: string (nullable = true)
 |-- TITLE: string (nullable = true)
 |-- TITLE_NAME: string (nullable = true)
 |-- TITLE_CLEAN: string (nullable = true)
 |-- SKILLS: string (nullable = true)
 |-- SKILLS_NAME: string (nullable = true)
 |-- SPECIALIZED_SKILLS: string (nullable = true)
 |-- SPECIALIZED_SKILLS_NAME: string (nullable = true)
 |-- CERTIFICATIONS: string (nullable = true)
 |-- CERTIFICATIONS_NAME: string (nullable = true)
 |-- COMMON_SKILLS: string (nullable = true)
 |-- COMMON_SKILLS_NAME: string (nullable = true)
 |-- SOFTWARE_SKILLS: string (nullable = true)
 |-- SOFTWARE_SKILLS_NAME: string (nullable = true)
 |-- ONET: string (nullable = true)
 |-- ONET_NAME: string (nullable = true)
 |-- ONET_2019: string (nullable = true)
 |-- ONET_2019_NAME: string (nullable = true)
 |-- CIP6: string (nullable = true)
 |-- CIP6_NAME: string (nullable = true)
 |-- CIP4: string (nullable = true)
 |-- CIP4_NAME: string (nullable = true)
 |-- CIP2: string (nullable = true)
 |-- CIP2_NAME: string (nullable = true)
 |-- SOC_2021_2: string (nullable = true)
 |-- SOC_2021_2_NAME: string (nullable = true)
 |-- SOC_2021_3: string (nullable = true)
 |-- SOC_2021_3_NAME: string (nullable = true)
 |-- SOC_2021_4: string (nullable = true)
 |-- SOC_2021_4_NAME: string (nullable = true)
 |-- SOC_2021_5: string (nullable = true)
 |-- SOC_2021_5_NAME: string (nullable = true)
 |-- LOT_CAREER_AREA: integer (nullable = true)
 |-- LOT_CAREER_AREA_NAME: string (nullable = true)
 |-- LOT_OCCUPATION: integer (nullable = true)
 |-- LOT_OCCUPATION_NAME: string (nullable = true)
 |-- LOT_SPECIALIZED_OCCUPATION: integer (nullable = true)
 |-- LOT_SPECIALIZED_OCCUPATION_NAME: string (nullable = true)
 |-- LOT_OCCUPATION_GROUP: integer (nullable = true)
 |-- LOT_OCCUPATION_GROUP_NAME: string (nullable = true)
 |-- LOT_V6_SPECIALIZED_OCCUPATION: integer (nullable = true)
 |-- LOT_V6_SPECIALIZED_OCCUPATION_NAME: string (nullable = true)
 |-- LOT_V6_OCCUPATION: integer (nullable = true)
 |-- LOT_V6_OCCUPATION_NAME: string (nullable = true)
 |-- LOT_V6_OCCUPATION_GROUP: integer (nullable = true)
 |-- LOT_V6_OCCUPATION_GROUP_NAME: string (nullable = true)
 |-- LOT_V6_CAREER_AREA: integer (nullable = true)
 |-- LOT_V6_CAREER_AREA_NAME: string (nullable = true)
 |-- SOC_2: string (nullable = true)
 |-- SOC_2_NAME: string (nullable = true)
 |-- SOC_3: string (nullable = true)
 |-- SOC_3_NAME: string (nullable = true)
 |-- SOC_4: string (nullable = true)
 |-- SOC_4_NAME: string (nullable = true)
 |-- SOC_5: string (nullable = true)
 |-- SOC_5_NAME: string (nullable = true)
 |-- LIGHTCAST_SECTORS: string (nullable = true)
 |-- LIGHTCAST_SECTORS_NAME: string (nullable = true)
 |-- NAICS_2022_2: integer (nullable = true)
 |-- NAICS_2022_2_NAME: string (nullable = true)
 |-- NAICS_2022_3: integer (nullable = true)
 |-- NAICS_2022_3_NAME: string (nullable = true)
 |-- NAICS_2022_4: integer (nullable = true)
 |-- NAICS_2022_4_NAME: string (nullable = true)
 |-- NAICS_2022_5: integer (nullable = true)
 |-- NAICS_2022_5_NAME: string (nullable = true)
 |-- NAICS_2022_6: integer (nullable = true)
 |-- NAICS_2022_6_NAME: string (nullable = true)

[Stage 2:>                                                          (0 + 1) / 1]

+--------------------+-----------------+----------------------+----------+--------+---------+--------+--------------------+--------------------+--------------------+-----------+-------------------+--------------------+--------------------+---------------+----------------+--------+--------------------+-----------+-------------------+----------------+---------------------+-------------+-------------------+-------------+------------------+---------------+--------------------+--------------------+--------------------+-------------+------+-----------+----------------+-------------------+---------+-----------+--------------------+--------------------+-------------+------+--------------+-----+--------------------+-----+----------+---------------+--------------------+---------------+--------------------+------------+--------------------+------------+--------------------+------+--------------------+------+--------------------+------+--------------------+------+--------------------+------+--------------------+------------------+-------------------+--------------------+--------------------+--------------------+--------------------+-----------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+----------+--------------------+----------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+----------+--------------------+----------+--------------------+----------+---------------+----------+---------------+---------------+--------------------+--------------+--------------------+--------------------------+-------------------------------+--------------------+-------------------------+-----------------------------+----------------------------------+-----------------+----------------------+-----------------------+----------------------------+------------------+-----------------------+-------+--------------------+-------+--------------------+-------+---------------+-------+---------------+-----------------+----------------------+------------+--------------------+------------+--------------------+------------+--------------------+------------+--------------------+------------+--------------------+
|                  ID|LAST_UPDATED_DATE|LAST_UPDATED_TIMESTAMP|DUPLICATES|  POSTED|  EXPIRED|DURATION|        SOURCE_TYPES|             SOURCES|                 URL|ACTIVE_URLS|ACTIVE_SOURCES_INFO|           TITLE_RAW|                BODY|MODELED_EXPIRED|MODELED_DURATION| COMPANY|        COMPANY_NAME|COMPANY_RAW|COMPANY_IS_STAFFING|EDUCATION_LEVELS|EDUCATION_LEVELS_NAME|MIN_EDULEVELS| MIN_EDULEVELS_NAME|MAX_EDULEVELS|MAX_EDULEVELS_NAME|EMPLOYMENT_TYPE|EMPLOYMENT_TYPE_NAME|MIN_YEARS_EXPERIENCE|MAX_YEARS_EXPERIENCE|IS_INTERNSHIP|SALARY|REMOTE_TYPE|REMOTE_TYPE_NAME|ORIGINAL_PAY_PERIOD|SALARY_TO|SALARY_FROM|            LOCATION|                CITY|    CITY_NAME|COUNTY|   COUNTY_NAME|  MSA|            MSA_NAME|STATE|STATE_NAME|COUNTY_OUTGOING|COUNTY_NAME_OUTGOING|COUNTY_INCOMING|COUNTY_NAME_INCOMING|MSA_OUTGOING|   MSA_NAME_OUTGOING|MSA_INCOMING|   MSA_NAME_INCOMING|NAICS2|         NAICS2_NAME|NAICS3|         NAICS3_NAME|NAICS4|         NAICS4_NAME|NAICS5|         NAICS5_NAME|NAICS6|         NAICS6_NAME|             TITLE|         TITLE_NAME|         TITLE_CLEAN|              SKILLS|         SKILLS_NAME|  SPECIALIZED_SKILLS|SPECIALIZED_SKILLS_NAME|      CERTIFICATIONS| CERTIFICATIONS_NAME|       COMMON_SKILLS|  COMMON_SKILLS_NAME|     SOFTWARE_SKILLS|SOFTWARE_SKILLS_NAME|      ONET|           ONET_NAME| ONET_2019|      ONET_2019_NAME|                CIP6|           CIP6_NAME|                CIP4|           CIP4_NAME|                CIP2|           CIP2_NAME|SOC_2021_2|     SOC_2021_2_NAME|SOC_2021_3|     SOC_2021_3_NAME|SOC_2021_4|SOC_2021_4_NAME|SOC_2021_5|SOC_2021_5_NAME|LOT_CAREER_AREA|LOT_CAREER_AREA_NAME|LOT_OCCUPATION| LOT_OCCUPATION_NAME|LOT_SPECIALIZED_OCCUPATION|LOT_SPECIALIZED_OCCUPATION_NAME|LOT_OCCUPATION_GROUP|LOT_OCCUPATION_GROUP_NAME|LOT_V6_SPECIALIZED_OCCUPATION|LOT_V6_SPECIALIZED_OCCUPATION_NAME|LOT_V6_OCCUPATION|LOT_V6_OCCUPATION_NAME|LOT_V6_OCCUPATION_GROUP|LOT_V6_OCCUPATION_GROUP_NAME|LOT_V6_CAREER_AREA|LOT_V6_CAREER_AREA_NAME|  SOC_2|          SOC_2_NAME|  SOC_3|          SOC_3_NAME|  SOC_4|     SOC_4_NAME|  SOC_5|     SOC_5_NAME|LIGHTCAST_SECTORS|LIGHTCAST_SECTORS_NAME|NAICS_2022_2|   NAICS_2022_2_NAME|NAICS_2022_3|   NAICS_2022_3_NAME|NAICS_2022_4|   NAICS_2022_4_NAME|NAICS_2022_5|   NAICS_2022_5_NAME|NAICS_2022_6|   NAICS_2022_6_NAME|
+--------------------+-----------------+----------------------+----------+--------+---------+--------+--------------------+--------------------+--------------------+-----------+-------------------+--------------------+--------------------+---------------+----------------+--------+--------------------+-----------+-------------------+----------------+---------------------+-------------+-------------------+-------------+------------------+---------------+--------------------+--------------------+--------------------+-------------+------+-----------+----------------+-------------------+---------+-----------+--------------------+--------------------+-------------+------+--------------+-----+--------------------+-----+----------+---------------+--------------------+---------------+--------------------+------------+--------------------+------------+--------------------+------+--------------------+------+--------------------+------+--------------------+------+--------------------+------+--------------------+------------------+-------------------+--------------------+--------------------+--------------------+--------------------+-----------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+----------+--------------------+----------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+----------+--------------------+----------+--------------------+----------+---------------+----------+---------------+---------------+--------------------+--------------+--------------------+--------------------------+-------------------------------+--------------------+-------------------------+-----------------------------+----------------------------------+-----------------+----------------------+-----------------------+----------------------------+------------------+-----------------------+-------+--------------------+-------+--------------------+-------+---------------+-------+---------------+-----------------+----------------------+------------+--------------------+------------+--------------------+------------+--------------------+------------+--------------------+------------+--------------------+
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|0cb072af26757b6c4...|         8/2/2024|  2024-08-02 17:08:...|         0|6/2/2024| 8/1/2024|    NULL| [\n  "Job Board"\n]| [\n  "maine.gov"\n]|[\n  "https://job...|         []|               NULL|Oracle Consultant...|Oracle Consultant...|       8/1/2024|            NULL|  133098|Smx Corporation L...|        SMX|               true|      [\n  99\n]| [\n  "No Educatio...|           99|No Education Listed|         NULL|              NULL|              1|Full-time (> 32 h...|                   3|                   3|        false|  NULL|          1|          Remote|               NULL|     NULL|       NULL|{\n  "lat": 44.31...|    QXVndXN0YSwgTUU=|  Augusta, ME| 23011|  Kennebec, ME|12300|Augusta-Watervill...|   23|     Maine|          23011|        Kennebec, ME|          23011|        Kennebec, ME|       12300|Augusta-Watervill...|       12300|Augusta-Watervill...|    56|Administrative an...|   561|Administrative an...|  5613| Employment Services| 56132|Temporary Help Se...|561320|Temporary Help Se...|ET21DDA63780A7DC09| Oracle Consultants|oracle consultant...|[\n  "KS122626T55...|[\n  "Procurement...|[\n  "KS122626T55...|   [\n  "Procurement...|                  []|                  []|                  []|                  []|[\n  "BGSBF3F508F...|[\n  "Oracle Busi...|15-2051.01|Business Intellig...|15-2051.01|Business Intellig...|                  []|                  []|                  []|                  []|                  []|                  []|   15-0000|Computer and Math...|   15-2000|Mathematical Scie...|   15-2050|Data Scientists|   15-2051|Data Scientists|             23|Information Techn...|        231010|Business Intellig...|                  23101012|           Oracle Consultant...|                2310|     Business Intellig...|                     23101012|              Oracle Consultant...|           231010|  Business Intellig...|                   2310|        Business Intellig...|                23|   Information Techn...|15-0000|Computer and Math...|15-2000|Mathematical Scie...|15-2050|Data Scientists|15-2051|Data Scientists|             NULL|                  NULL|          56|Administrative an...|         561|Administrative an...|        5613| Employment Services|       56132|Temporary Help Se...|      561320|Temporary Help Se...|
|85318b12b3331fa49...|         9/6/2024|  2024-09-06 20:32:...|         1|6/2/2024| 7/7/2024|      35| [\n  "Job Board"\n]|[\n  "dejobs.org"\n]|[\n  "https://dej...|         []|               NULL|        Data Analyst|Taking care of pe...|      6/10/2024|               8|39063746|            Sedgwick|   Sedgwick|              false|       [\n  2\n]| [\n  "Bachelor's ...|            2|  Bachelor's degree|         NULL|              NULL|              1|Full-time (> 32 h...|                   5|                NULL|        false|  NULL|          0|          [None]|               NULL|     NULL|       NULL|{\n  "lat": 32.77...|    RGFsbGFzLCBUWA==|   Dallas, TX| 48113|    Dallas, TX|19100|Dallas-Fort Worth...|   48|     Texas|          48113|          Dallas, TX|          48113|          Dallas, TX|       19100|Dallas-Fort Worth...|       19100|Dallas-Fort Worth...|    52|Finance and Insur...|   524|Insurance Carrier...|  5242|Agencies, Brokera...| 52429|Other Insurance R...|524291|    Claims Adjusting|ET3037E0C947A02404|      Data Analysts|        data analyst|[\n  "KS1218W78FG...|[\n  "Management"...|[\n  "ESF3939CE1F...|   [\n  "Exception R...|[\n  "KS683TN76T7...|[\n  "Security Cl...|[\n  "KS1218W78FG...|[\n  "Management"...|[\n  "KS126HY6YLT...|[\n  "Microsoft O...|15-2051.01|Business Intellig...|15-2051.01|Business Intellig...|                  []|                  []|                  []|                  []|                  []|                  []|   15-0000|Computer and Math...|   15-2000|Mathematical Scie...|   15-2050|Data Scientists|   15-2051|Data Scientists|             23|Information Techn...|        231113|Data / Data Minin...|                  23111310|                   Data Analyst|                2311|     Data Analysis and...|                     23111310|                      Data Analyst|           231113|  Data / Data Minin...|                   2311|        Data Analysis and...|                23|   Information Techn...|15-0000|Computer and Math...|15-2000|Mathematical Scie...|15-2050|Data Scientists|15-2051|Data Scientists|             NULL|                  NULL|          52|Finance and Insur...|         524|Insurance Carrier...|        5242|Agencies, Brokera...|       52429|Other Insurance R...|      524291|    Claims Adjusting|
|1b5c3941e54a1889e...|         9/6/2024|  2024-09-06 20:32:...|         1|6/2/2024|7/20/2024|      48| [\n  "Job Board"\n]|[\n  "disabledper...|[\n  "https://www...|         []|               NULL|Sr. Lead Data Mgm...|About this role:\...|      6/12/2024|              10|37615159|         Wells Fargo|Wells Fargo|              false|      [\n  99\n]| [\n  "No Educatio...|           99|No Education Listed|         NULL|              NULL|              1|Full-time (> 32 h...|                   3|                NULL|        false|  NULL|          0|          [None]|               NULL|     NULL|       NULL|{\n  "lat": 33.44...|    UGhvZW5peCwgQVo=|  Phoenix, AZ|  4013|  Maricopa, AZ|38060|Phoenix-Mesa-Chan...|    4|   Arizona|           4013|        Maricopa, AZ|           4013|        Maricopa, AZ|       38060|Phoenix-Mesa-Chan...|       38060|Phoenix-Mesa-Chan...|    52|Finance and Insur...|   522|Credit Intermedia...|  5221|Depository Credit...| 52211|  Commercial Banking|522110|  Commercial Banking|ET2114E0404BA30075|Management Analysts|sr lead data mgmt...|[\n  "KS123QX62QY...|[\n  "Exit Strate...|[\n  "KS123QX62QY...|   [\n  "Exit Strate...|                  []|                  []|[\n  "KS7G6NP6R6L...|[\n  "Reliability...|[\n  "KS4409D76NW...|[\n  "SAS (Softwa...|15-2051.01|Business Intellig...|15-2051.01|Business Intellig...|                  []|                  []|                  []|                  []|                  []|                  []|   15-0000|Computer and Math...|   15-2000|Mathematical Scie...|   15-2050|Data Scientists|   15-2051|Data Scientists|             23|Information Techn...|        231113|Data / Data Minin...|                  23111310|                   Data Analyst|                2311|     Data Analysis and...|                     23111310|                      Data Analyst|           231113|  Data / Data Minin...|                   2311|        Data Analysis and...|                23|   Information Techn...|15-0000|Computer and Math...|15-2000|Mathematical Scie...|15-2050|Data Scientists|15-2051|Data Scientists|        [\n  6\n]|  [\n  "Data Privac...|          52|Finance and Insur...|         522|Credit Intermedia...|        5221|Depository Credit...|       52211|  Commercial Banking|      522110|  Commercial Banking|
|cb5ca25f02bdf25c1...|        6/19/2024|   2024-06-19 07:00:00|         0|6/2/2024|6/17/2024|      15|[\n  "FreeJobBoar...|[\n  "craigslist....|[\n  "https://mod...|         []|               NULL|Comisiones de $10...|Comisiones de $10...|      6/17/2024|              15|       0|        Unclassified|      LH/GM|              false|      [\n  99\n]| [\n  "No Educatio...|           99|No Education Listed|         NULL|              NULL|              3|Part-time / full-...|                NULL|                NULL|        false| 92500|          0|          [None]|               year|   150000|      35000|{\n  "lat": 37.63...|    TW9kZXN0bywgQ0E=|  Modesto, CA|  6099|Stanislaus, CA|33700|         Modesto, CA|    6|California|           6099|      Stanislaus, CA|           6099|      Stanislaus, CA|       33700|         Modesto, CA|       33700|         Modesto, CA|    99|Unclassified Indu...|   999|Unclassified Indu...|  9999|Unclassified Indu...| 99999|Unclassified Indu...|999999|Unclassified Indu...|ET0000000000000000|       Unclassified|comisiones de por...|                  []|                  []|                  []|                     []|                  []|                  []|                  []|                  []|                  []|                  []|15-2051.01|Business Intellig...|15-2051.01|Business Intellig...|                  []|                  []|                  []|                  []|                  []|                  []|   15-0000|Computer and Math...|   15-2000|Mathematical Scie...|   15-2050|Data Scientists|   15-2051|Data Scientists|             23|Information Techn...|        231010|Business Intellig...|                  23101012|           Oracle Consultant...|                2310|     Business Intellig...|                     23101012|              Oracle Consultant...|           231010|  Business Intellig...|                   2310|        Business Intellig...|                23|   Information Techn...|15-0000|Computer and Math...|15-2000|Mathematical Scie...|15-2050|Data Scientists|15-2051|Data Scientists|             NULL|                  NULL|          99|Unclassified Indu...|         999|Unclassified Indu...|        9999|Unclassified Indu...|       99999|Unclassified Indu...|      999999|Unclassified Indu...|
+--------------------+-----------------+----------------------+----------+--------+---------+--------+--------------------+--------------------+--------------------+-----------+-------------------+--------------------+--------------------+---------------+----------------+--------+--------------------+-----------+-------------------+----------------+---------------------+-------------+-------------------+-------------+------------------+---------------+--------------------+--------------------+--------------------+-------------+------+-----------+----------------+-------------------+---------+-----------+--------------------+--------------------+-------------+------+--------------+-----+--------------------+-----+----------+---------------+--------------------+---------------+--------------------+------------+--------------------+------------+--------------------+------+--------------------+------+--------------------+------+--------------------+------+--------------------+------+--------------------+------------------+-------------------+--------------------+--------------------+--------------------+--------------------+-----------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+----------+--------------------+----------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+----------+--------------------+----------+--------------------+----------+---------------+----------+---------------+---------------+--------------------+--------------+--------------------+--------------------------+-------------------------------+--------------------+-------------------------+-----------------------------+----------------------------------+-----------------+----------------------+-----------------------+----------------------------+------------------+-----------------------+-------+--------------------+-------+--------------------+-------+---------------+-------+---------------+-----------------+----------------------+------------+--------------------+------------+--------------------+------------+--------------------+------------+--------------------+------------+--------------------+
only showing top 5 rows

2 Data Preparation

# Step 1: Casting salary and experience columns
df = (
    df
    .withColumn("SALARY", col("SALARY").cast("float"))
    .withColumn("SALARY_FROM", col("SALARY_FROM").cast("float"))
    .withColumn("SALARY_TO", col("SALARY_TO").cast("float"))
    .withColumn("MIN_YEARS_EXPERIENCE", col("MIN_YEARS_EXPERIENCE").cast("float"))
    .withColumn("MAX_YEARS_EXPERIENCE", col("MAX_YEARS_EXPERIENCE").cast("float"))
)

# Step 2: Computing medians for salary columns
def compute_median(sdf, col_name):
    q= sdf.approxQuantile(col_name, [0.5], 0.01)
    return q[0] if q else None

median_from = compute_median(df, "SALARY_FROM")
median_to = compute_median(df, "SALARY_TO")
median_salary = compute_median(df, "SALARY")

print("Medians:", median_from, median_to, median_salary)

# Step 3: Imputing missing salaries, but not experience
df = df.fillna({
    "SALARY_FROM" : median_from,
    "SALARY_TO" : median_to,
    "SALARY" : median_salary
})
df_filtered = df.filter(
    (F.col("SALARY") > 0) &
    F.col("EMPLOYMENT_TYPE_NAME").isNotNull() &
    (F.trim(F.col("EMPLOYMENT_TYPE_NAME")) != F.lit("")) &
    (F.lower(F.trim(F.col("EMPLOYMENT_TYPE_NAME"))) != F.lit("none"))
)

pdf = (
    df_filtered
      .select("EMPLOYMENT_TYPE_NAME", "SALARY")
      .toPandas()
)



# Step 5: Computing average salary
df = df.withColumn("Average_Salary", (col("SALARY_FROM") + col("SALARY_TO")) / 2)

# Step 6: Selecting Required Columns
export_cols = [
  "EDUCATION_LEVELS_NAME",
  "REMOTE_TYPE_NAME",
  "MAX_YEARS_EXPERIENCE",
  "Average_Salary",
  "median_salary"
  "LOT_V6_SPECIALIZED_OCCUPATION_NAME",
]

df_selected = df.select(*[c for c in export_cols if c in df.columns])

# Step 7: Saving to CSV
pdf = df_selected.toPandas()   # OK for small/medium data
pdf.to_csv("./data/lightcast_cleaned.csv", index=False)

print("Data Cleaning Complete. Rows retained:", len(pdf))

[Stage 3:>                                                          (0 + 1) / 1]                                                                                [Stage 4:>                                                          (0 + 1) / 1]                                                                                [Stage 5:>                                                          (0 + 1) / 1]

Medians: 87295.0 130042.0 115024.0

[Stage 6:>                                                          (0 + 1) / 1]                                                                                [Stage 7:>                                                          (0 + 1) / 1]

Data Cleaning Complete. Rows retained: 72498

3 Salary Distribution by Industry and Employment Type

# Salary Distribution by Industry and Employment Type

import os, re
import pandas as pd
import plotly.express as px
from IPython.display import display

# Ensure output folder exists
os.makedirs("output", exist_ok=True)

# 1) Filter out missing or zero salary values and bring to pandas
pdf = (
    df.filter(df["SALARY"] > 0)    # PySpark filter (assumes SALARY is numeric)
      .select("EMPLOYMENT_TYPE_NAME", "SALARY")
      .toPandas()
)

# Optional one-time preview (set True to debug)
SHOW_PREVIEW = False
if SHOW_PREVIEW:
    display(pdf.head(10))

# 2) Clean employment type names (overwrite the SAME column; no new column)
pdf["EMPLOYMENT_TYPE_NAME"] = (
    pdf["EMPLOYMENT_TYPE_NAME"]
      .astype(str)
      .apply(lambda x: re.sub(r"[^\x00-\x7F]+", "", x))
)

# Make sure salary is numeric (just in case)
pdf["SALARY"] = pd.to_numeric(pdf["SALARY"], errors="coerce")

# If a stray pluralized column exists from earlier runs, drop it
if "EMPLOYMENT_TYPE_NAMES" in pdf.columns:
    pdf = pdf.drop(columns=["EMPLOYMENT_TYPE_NAMES"])

# 3) Compute median salary for sorting
median_salaries = pdf.groupby("EMPLOYMENT_TYPE_NAME", dropna=False)["SALARY"].median()

# 4) Sort employment types based on median salary (descending)
sorted_employment_types = median_salaries.sort_values(ascending=False).index

# 5) Apply sorted categories
pdf["EMPLOYMENT_TYPE_NAME"] = pd.Categorical(
    pdf["EMPLOYMENT_TYPE_NAME"],
    categories=sorted_employment_types,
    ordered=True
)

# 6) Create box plot (single neutral color)
fig = px.box(
    pdf,
    x="EMPLOYMENT_TYPE_NAME",
    y="SALARY",
    title="Salary Distribution by Employment Type",
    points="all"  # show all points
)
# Force black for markers and box lines
fig.update_traces(marker_color="black", line_color="black")

# 7) Improve layout, font styles, and axis labels
fig.update_layout(
    title=dict(text="Salary Distribution by Employment Type",
               font=dict(size=30, family="Arial", color="black")),
    xaxis=dict(
        showline=True, linewidth=2, linecolor="black", mirror=True, showgrid=False,
        categoryorder="array", categoryarray=list(sorted_employment_types)
    ),
    yaxis=dict(
        title=dict(text="Salary (K $)", font=dict(size=24, family="Arial", color="black")),
        tickvals=[0, 50000, 100000, 150000, 200000, 250000, 300000, 350000, 400000, 450000, 500000],
        ticktext=["0","50K","100K","150K","200K","250K","300K","350K","400K","450K","500K"],
        tickfont=dict(size=18, family="Arial", color="black"),
        showline=True, linewidth=2, linecolor="black", mirror=True,
        showgrid=True, gridcolor="lightgray", gridwidth=0.5
    ),
    font=dict(family="Arial", size=16, color="black"),
    boxgap=0.7, boxmode="group",
    plot_bgcolor="white", paper_bgcolor="white",
    showlegend=False, height=500, width=850,
)

# In Quarto, returning fig renders it once (no need for fig.show())
fig.show()


# Save SVG (fallback to HTML if kaleido isn't installed)
try:
    # pip install kaleido
    fig.write_image("output/Q1.svg", width=850, height=500, scale=1)
except Exception:
    fig.write_html("output/Q1.html", include_plotlyjs="cdn")

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